Recurrent subgraph prediction

Interactions in dynamic networks often transcend the dyadic barrier and emerge as subgraphs. The evolution of these subgraphs cannot be completely predicted using a pairwise link prediction analysis. We propose a novel solution to the problem - "Prediction of Recurrent Subgraphs (PReSub)" which treats subgraphs as individual entities in their own right. PReSub predicts re-occurring subgraphs using the network's vector space embedding and a set of "early warning subgraphs" which act as global and local descriptors of the subgraph's behavior. PReSub can be used as an out-of-the-box pipeline method with user-provided subgraphs or even to discover interesting subgraphs in an unsupervised manner. It can handle missing network information and is parallelizable. We show that PReSub outperforms traditional pairwise link prediction for a variety of evolving network datasets. The goal of this framework is to improve our understanding of subgraphs and provide an alternative representation in order to characterize their behavior.

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